Design of Comparative Experiments

The coherent framework behind good practice; for working statisticians, advanced undergraduates, beginning graduate students.

R. A. Bailey (Author)

9780521865067, Cambridge University Press

Hardback, published 17 April 2008

346 pages, 175 b/w illus. 110 tables 140 exercises
25.4 x 17.8 x 2.1 cm, 0.82 kg

'This excellent book clearly presents elegant, general and simplifying theory, combining valuable practical advice with a large number of real examples. It treats the design of comparative experiments with a unique approach not seen in other books …A must-read for anyone designing experiments or wanting to learn about the design of experiments.' Ching-Shui Cheng, University of California, Berkeley

This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.

Preface
1. Forward look
2. Unstructured experiments
3. Simple treatment structure
4. Blocking
5. Factorial treatment structure
6. Row-column designs
7. Experiments on people and animals
8. Small units inside large units
9. More about Latin squares
10. The calculus of factors
11. Incomplete-block designs
12. Factorial designs in incomplete blocks
13. Fractional factorial designs
14. Backward look
Exercises
Sources of examples, Questions and exercises
Further reading
Bibliography
Index.

Subject Areas: Scientific equipment, experiments & techniques [PDN], Probability & statistics [PBT]